2021 13th International Conference on Machine Learning and Computing最新文献_第2页

Accelerating Transformer for Neural Machine Translation 神经机器翻译加速变压器

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457711

Li Huang, Wenyu Chen, Hong Qu

{"title":"Accelerating Transformer for Neural Machine Translation","authors":"Li Huang, Wenyu Chen, Hong Qu","doi":"10.1145/3457682.3457711","DOIUrl":"https://doi.org/10.1145/3457682.3457711","url":null,"abstract":"Neural Machine Translation (NMT) models based on Transformer achieve promising progress in both translation quality and training speed. Such a strong framework adopts parallel structures that greatly improve the decoding speed without losing quality. However, due to the self-attention network in decoder that cannot maintain the parallelization under the auto-regressive scheme, the Transformer did not enjoy the same speed performance as training when inference. In this work, with simplicity and feasibility in mind, we introduce a gated cumulative attention network to replace the self-attention part in Transformer decoder to maintain the parallelization property in the inference phase. The gated cumulative attention network includes two sub-layers, a gated linearly cumulative layer that creates the relationship between already predicted tokens and current representation, and a feature fusion layer that enhances the representation with a feature fusion operation. The proposed method was evaluated on WMT17 datasets with 12 language pair groups. Experimental results show the effectiveness of the proposed method and also demonstrated that the proposed gated cumulative attention network has adequate ability as an alternative to the self-attention part in the Transformer decoder.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129134509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Mixed-coding Harmony Search Algorithm for the Closed Loop Layout Problem 闭环布局问题的混合编码和谐搜索算法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457696

Wenhan Deng, Ming Zhang, Kai He, Lijin Wang, Juan Lin, Yiwen Zhong

{"title":"A Mixed-coding Harmony Search Algorithm for the Closed Loop Layout Problem","authors":"Wenhan Deng, Ming Zhang, Kai He, Lijin Wang, Juan Lin, Yiwen Zhong","doi":"10.1145/3457682.3457696","DOIUrl":"https://doi.org/10.1145/3457682.3457696","url":null,"abstract":"Closed Loop Layout Problem (CLLP) is an NP-hard facility layout problem that determines the most favorable placement of facilities along a rectangle loop with adjustable size. The primary objective of the CLLP is to minimize the total transportation cost of the material flow between facilities. To obtain this objective, the optimal placement sequence of the facilities and the corresponding optimal size of the rectangle loop must be obtained at the same time. Although several metaheuristic-based methods have been proposed to tackle the CLLP, those methods only use metaheuristics to search the optimal placement sequence of facilities, and the optimal size of the rectangle loop is obtained by enumeration method. In order to improve the search efficiency of metaheuristics for the CLLP, this paper presents a Mixed-coding Harmony Search (MHS) algorithm which includes Permutation-based Discrete Harmony Search (PDHS) and Continuous Harmony Search (CHS). The PDHS part is designed to search the optimal placement sequence of facilities, and the CHS part is used to find the optimal size of the rectangle loop. Comparing experiments, which were conducted on 13 CLLP instances, have shown that the MHS algorithm obtains better results in less time than other existing metaheuristics.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129213362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Using Principal Component Analysis and Online Sequential Extreme Learning Machine Approach for Transient Electromagnetic Nonlinear Inversion: TEM-Inversion-based-on-PCA-OSELM

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457766

Ruiyou Li

{"title":"Using Principal Component Analysis and Online Sequential Extreme Learning Machine Approach for Transient Electromagnetic Nonlinear Inversion: TEM-Inversion-based-on-PCA-OSELM","authors":"Ruiyou Li","doi":"10.1145/3457682.3457766","DOIUrl":"https://doi.org/10.1145/3457682.3457766","url":null,"abstract":"The traditional artificial neural network based on gradient descent method result in low computational efficiency and local convergence for transient electromagnetic inversion. To solve the these problems, a hybrid approach combining principal component analysis (PCA) and online sequential extreme learning machine (OSELM) is proposed in this paper (PCA-OSELM) and is applied in the transient electromagnetic inversion. First, a principal component analysis method is introduced to reduce the dimension of vertical magnetic field data and improves the computational efficiency. Then, the new samples obtained from the data sets are added to the training samples as the next update information to establish the OSELM prediction models, so that improve the inversion accuracy. Finally, the inversion results of the two typical layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed approach can well solve the modeling nonlinear problem that high-dimensional data generated by transient electromagnetic method. Moreover, compared with other nonlinear inversion methods (OSELM, ELM), the PCA-OSELM achieves more accurate, better generalization ability and higher computational efficiency, which can provide new ideas for the application of neural networks in geophysical inversion.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126770743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Acoustic Classification of Bird Species Using Wavelets and Learning Algorithms 基于小波和学习算法的鸟类声学分类

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457692

Song Yang, R. Frier, Qiang Shi

引用次数: 2

Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations 基于下一项和最后一篮会话推荐的RNN和嵌入方法的比较

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457755

M. Salampasis, Theodosios Siomos, Alkiviadis Katsalis, K. Diamantaras, Konstantinos Christantonis, Marina Delianidi, Iphigenia Karaveli

{"title":"Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations","authors":"M. Salampasis, Theodosios Siomos, Alkiviadis Katsalis, K. Diamantaras, Konstantinos Christantonis, Marina Delianidi, Iphigenia Karaveli","doi":"10.1145/3457682.3457755","DOIUrl":"https://doi.org/10.1145/3457682.3457755","url":null,"abstract":"Recurrent Neural Networks (RNNs) have been shown to perform very effectively in session-based recommendation settings, when compared to other commonly used methods that consider the last viewed item of the user and precomputed item-to-item similarities. However, there is little systematic study on how RNNs perform in comparison to methods that use embeddings for item representation for Collaborative Filtering. In this paper we use two industry datasets to compare RNNs with other sequential recommenders that use various embedding methods to represent items. The first dataset corresponds to a typical e-commerce session-based scenario demanding effective next-item recommendation. The second dataset represents a last-basket prediction setting. Results show that although the RNN greatly outperforms embedding methods in the next-item scenario, the dynamic nature of the RNNs has not the same positive impact in the last-basket prediction task. We also present and test a framework that enables the hybrid utilization of text content and item sequences using embeddings. Finally, we report on experiments with reranking methods that demonstrate the effectiveness of simple and practical methods, using item categories, to improve the results.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Generalized Intersection over Union Based Online Weighted Multiple Instance Learning Algorithm for Object Tracking 基于广义交联的在线加权多实例学习目标跟踪算法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457698

Xiaoshun Lu, Si Chen, Zhuoyuan Zheng, Chenyu Weng, Rui Xu

{"title":"Generalized Intersection over Union Based Online Weighted Multiple Instance Learning Algorithm for Object Tracking","authors":"Xiaoshun Lu, Si Chen, Zhuoyuan Zheng, Chenyu Weng, Rui Xu","doi":"10.1145/3457682.3457698","DOIUrl":"https://doi.org/10.1145/3457682.3457698","url":null,"abstract":"The traditional weighted multiple instance learning based online object tracking methods often use the Euclidean distance between the centers of the bounding boxes of the target and the instance to weight the instances in the positive bag, which can not effectively measure the contribution degree of the instances of the positive and negative bags and easily causes the object drifting problem. This paper proposes a generalized intersection over union based online weighted multiple instance learning algorithm (named GIoU-WMIL) for object tracking. This algorithm introduces a novel generalized intersection over union (GIoU) to calculate the overlap degree between the bounding boxes of the target and each instance in the bags, in order to effectively measure the contribution of the different instances. Furthermore, a new objective function is designed by employing the GIoU-based weights of all the instances in the positive and negative bags. Experiments show that the proposed algorithm has the good robustness and accuracy on several challenging video sequences.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133358090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Gradient heatmap based Table Structure Recognition 基于梯度热图的表结构识别

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457752

Lingjun Kong, Yunchao Bao, Qianwen Wang, Lijun Cao, Shengmei Zhao

{"title":"A Gradient heatmap based Table Structure Recognition","authors":"Lingjun Kong, Yunchao Bao, Qianwen Wang, Lijun Cao, Shengmei Zhao","doi":"10.1145/3457682.3457752","DOIUrl":"https://doi.org/10.1145/3457682.3457752","url":null,"abstract":"Most methods to recognize the structure of a table are to use the object detection approach to directly locate each cell in the table or to segment the table line based on the fully convolutional network (FCN). The problem of the former is that it is laborious to recognize the distorted table, while the problem of the latter is that the sample imbalance makes it difficult to train the model. In this paper, a gradient heatmap based table structure recognition method is proposed, by exploring the gradient heatmaps of the vertical lines and horizontal lines in the table. Specifically, the pixels of the vertical lines of the table are obtained according to the gradient heatmap, then the pixels of the horizontal lines are obtained using the same method, and finally the table structure is restored by using the connected domain search method. Compared with the Single Shot MultiBox Detector (SSD) and Faster RCNN that directly detects cells, our Average Precision (AP) value reached up to 99.5%, which is much higher than the above models. Additionally, we demonstrate that the AP values of the proposed models are reduced almost negligibly when the IoU threshold increased from 0.5 to 0.75, while the AP value of the fast RCNN and SSD model decreased significantly.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125948020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intra-Class Cutmix for Unbalanced Data Augmentation 不平衡数据增强的类内混合

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457719

Caidan Zhao, Yang Lei

引用次数: 6

Tracking Ground Targets with Road Constraint Using a Gaussian Mixture Road-Labeled PHD Filter 基于高斯混合道路标记PHD滤波器的道路约束地面目标跟踪

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457738

Jihong Zheng, Jinming Min, He He

引用次数: 0

An Efficacious Method for Facial Expression Recognition: GAN Erased Facial Feature Network (GE2FN) 一种有效的面部表情识别方法:GAN擦除面部特征网络(GE2FN)

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457746

Tao Zhang, Tang Kai

引用次数: 1